Rock Mass Classification Research Articles

Rock Mass Structure Classification of Caves Based on the 3D Rock Block Index

In large-scale water conservancy and hydropower projects, complex rock structures are considered to be the main factor controlling the stability of hydraulic structures. The classification of rock mass structure plays an important role in the safety of all kinds of large buildings, especially underground engineering buildings. As a quantitative classification index of rock mass, the rock block index is very common in the classification of borehole and dam foundation rock mass structures. However, there are few studies on the classification of underground engineering rock masses. Moreover, their classification criteria have disadvantages in spatial dimension. Therefore, this paper takes the long exploratory cave CPD1 in the water transmission and power generation system of the Qingtian pumped storage power station in Zhejiang Province as the research object and launches a study on the structural classification of the rock mass of a flat cave based on the 3D rock block index. According to the group distribution of joints, the sections are statistically homogeneous. Additionally, the Monte Carlo method is used to carry out random simulations to generate a three-dimensional joint network model. The virtual survey lines are arranged along the center of the shape of the three different orthogonal planes of the 3D joint network model to represent the boreholes, and the RBI values of the virtual survey lines on each orthogonal plane are counted to classify the rock mass structure of the flat cave in a refined manner using the rock block index of the rock mass in 3D. The above method realizes the application of the 3D rock block index in underground engineering and overcomes the limitations of traditional rock mass classification methods in terms of classification index and dimension. The results show that: (1) Three-dimensional joint network simulations built on statistical and probabilistic foundations can visualize the structure of the rock mass and more accurately reflect the structural characteristics of the actual rock mass. (2) Based on the 3D rock block index, the rock mass structure of the long-tunnel CPD1 is classified, from that of a continuous structure to a blocky structure, corresponding to the integrity of the rock mass from complete to relatively complete. The classification results are consistent with the evaluation results of horizontal tunnel seismic wave geophysical exploration. (3) Based on the 3D joint network model, it is reasonable and feasible to use the 3D rock block index as a quantitative evaluation index to determine the structure type of flat cave rock masses. The above method is helpful and significant in the classification of underground engineering rock mass structures.

Support design in underground coal mines using modified rock mass classification system (RMRdyn) for enhanced safety– an approach from stable and failed roof cases

Support design in underground coal mines using modified rock mass classification system (RMRdyn) for enhanced safety– an approach from stable and failed roof cases

Comparative Study of Rock Mass Classification Systems: A Case Study of Nagdhunga-Naubisea Road Tunnel

Ground classification systems have been widely used to characterize the soil or rock mass for the analysis and design of underground structures. Deformation characteristics of rock mass around the underground excavation boundary varies according to types of rock and their properties, in situ stress condition and types of support used. Broadly used classification systems for underground structures are Rock Mass Rating (RMR) and Rock Quality Index (Q- Systems) but in-case of the Nagdhunga-Naubisea road tunnel Nippon Expressway Company Limited used NEXCO-System. Literally, the NEXCO classification is based on the velocity of elastic wave, geological condition, boring core condition, competence factor, stability of tunnel face and convergence. However, in-case of Nagdhunga-Naubisea road tunnel, grade point is computed using the similar parameters that are used in RMR- System and Q-System. This research focuses about comparison between ground classification from RMR, Q-system and NEXCO system. The geological descriptions obtained from project office at chainages (0+497 to 0+502), (0+508 to 0+513), (0+581.8 to 0+587.8) and (0+584.2 to 0+590.2) were used to correlate the relationship between RMR, Q-system and NEXCO system of rock mass classification systems. Results showed that from RMR, the ground classification is “poor” at all the chainages whereas from Q-system is “very poor” at chainage (0+508 to 0+513) and “poor” at other chainages. Similarly, from NEXCO- system it was found that “DII” at chainages (0+497 to 0+502) and (0+508 to 0+513) and CII for chainages (0+581.8 to 0+587.8) and (0+584.2 to 0+590.2). These results revealed that the ground classification from NEXCO as DII is similar to poor from RMR and poor or very poor from Q system where as CII from NEXCO is similar to poor from both RMR and Q system.

Development of Rock Classification Systems: A Comprehensive Review with Emphasis on Artificial Intelligence Techniques

At the initial phases of tunnel design, information on rock properties is often limited. In such instances, the engineering classification of the rock is recommended as a primary assessment of its geotechnical condition. This paper reviews different rock mass classification methods in the tunnel industry. First, some important considerations for the classification of rock are discussed, such as rock quality designation (RQD), uniaxial compressive strength (UCS) and groundwater condition. Traditional rock classification methods are then assessed, including the rock structure rating (RSR), rock mass rating (RMR), rock mass index (RMI), geological strength index (GSI) and tunnelling quality index (Q system). As RMR and the Q system are two commonly used methods, the relationships between them are summarized and explored. Subsequently, we introduce the detailed application of artificial intelligence (AI) method on rock classification. The advantages and limitations of traditional methods and artificial intelligence (AI) methods are indicated, and their application scopes are clarified. Finally, we provide suggestions for the selection of rock classification methods and prospect the possible future research trends.

Performance Evaluation of Tunnel Support System in Jointed Rock Mass by Numerical Simulation: A Case Study

In this study, a numerical verification of the performance of preliminary tunnel support systems suggested by rock mass classification methods is performed. The Rock Mass Rating (RMR) method and the Quality Index (Q Barton) show a succession of a moderately jointed and jointed quality of Kherrata Tunnel 3. The numerical assessment combining the Finite Element Method (FEM) with the Discrete Fracture Network (DFN) models; involves the jointed area of the tunnel, in which the Voronoi model represents the joints distribution. During numerical simulations, particular attention focused on the evolution of the total displacement and the extension of the plastic zone. In addition, a parametric study was carried out to study the effect of the length and spacing of rock bolts on tunnel deformation. Results showed that the combined FEM and DFN models represent a solution for the stability analysis of underground excavations. The Voronoi model is suitable for representing the global distribution of joints in the rock mass. The parametric study carried out revealed pertinent conclusions. Thus, increasing the length of bolts (4 m to 6 m) decreases the total displacement of the tunnel; and reduces the extension of the plastic zone. However, the spacing between rock bolts does not improve the stability. The empirical method remains a reliable tool for the recommendations of preliminary support systems.

ANDESITE SLOPE STABILITY ANALYSIS USING ROCK MASS RATING (RMR) AND SLOPE MASS RATING (SMR) IN GUNUNG BATU AND GRAHA PUSPA AREAS, WEST BANDUNG REGENCY, WEST JAVA, INDONESIA

The susceptibility of West Java to landslides, coupled with its high population density, prompts research into landslide potential in the region. Notably, Gunung Batu and Graha Puspa in West Bandung Regency feature substantial andesite slopes situated along the Lembang fault and serve as tourist attractions. This study aims to assess the stability of these slopes using rock mass rating (RMR) and slope mass rating (SMR). The analysis integrates methods like rock mass classification (RMR), kinematics analysis, and empirical slope stability analysis (SMR), drawing on data obtained from uniaxial compressive strength (UCS) tests and scanline surveys. The andesite slopes exhibit RMR values ranging from 74 to 81, indicating good to very good rock mass quality. Design parameters and engineering properties are identified, including rock mass cohesion and internal friction angle, suggesting safe cut slope angles. Specific slopes are found susceptible to toppling or wedge failures. SMR values range from 72 to 96, categorizing the slopes as class I-II, indicating stability and a low probability of failure. Minor reinforcement options like scaling, toe ditch, fence, and spot bolting are proposed. Considering West Java’s seismic and rainfall risks, the study recommends further modelling to incorporate variations in seismic acceleration and water content. The method would yield safety factors and cut-off values under different slope conditions, enhancing the understanding and management of landslide risks in the region.

The Geomechanics Classification of Rock Mass with Using RMR and CRMR Method To Determine the Stability of Some Karst Caves in Haditha Area, Western Iraq

The Geomechanics Classification of Rock Mass with Using RMR and CRMR Method To Determine the Stability of Some Karst Caves in Haditha Area, Western Iraq

A Rock Mass Classification Method for Tuff Tunnel Based on the High-Pressure Gas Expansion Method

A precise rock mass grade result is crucial for directing the tunnel excavation engineering design. A novel rock mass classification method for tuff tunnel based on the high-pressure gas expansion method (HPGEM) was proposed, which was primarily built on field test data previously acquired by the research team. The main achievements are as follows: combined with field data and the HPGEM rock-breaking theory, analyzing the rock uniaxial compressive strength, rock mass integrity index, and the relationship between the gas generator unit consumption and fitted the relevant equations. After that, the rock optimal uniaxial compressive strength (about 150 MPa) and the rock integrity factor (about 0.85) were obtained. With reference to the BQ rock mass classification method, a new rock mass classification method that applied to HPGEM was proposed. This study fills the gap of the appropriate rock mass classification method requested on HPGEM.

Comparison of Tunnel Construction Cycle with NTNU Model for the Headrace Tunnel of Seti Khola Hydropower Project

Achieving smooth and efficient blasting of tunnel walls is a challenging task. Tunneling performance and advancement can best be performed with proper and effective blasting techniques in drill and blast techniques. In this manuscript, the Seti Khola hydropower project's headrace tunnel's real-time statistics are used to compare the overall performance of tunnel excavation for the widely used blasting approach. The headrace tunnel's 663 meters of observed and recorded length comprises rocks with weak to medium blastibility. According to the Q-system, this tunnel section's rock mass quality class ranges from good to bad. The rock type found has a monotonous sequence of metasandstone and phyllite with quartz partings and discontinuities filled with clay to silt. A variation in the excavation cycle is observed in relation to the presence of different rock mass classes. Ultimately, the NTNU model is compared and analysis is done on the drilling patterns and cycles of tunnel building. It was discovered that using the NTNU model for tunnel excavation presents a significant opportunity to enhance performance.

Study of Tunnel Support Methods Based on RMR and Q-System Methods at Kerinci Merangin Hydropower Jambi Province

Excavation of tunnel construction at the Kerinci Merangin Hydropower in Jambi Province is a complex job, because it will use the tunnel as a waterway which will always be faced with unstable conditions. One of the way to deal with instability is to classify the mass and determine the type of support on the roof and walls using empirical methods. The parameters in rock mass classification include mapping the structure, physical and mechanical properties of the material. Empirical analysis was carried out using the Rock Mass Rating (RMR) classification and the Q-System to determine the condition of the rock mass in the tunnel and determine the type of support that will be used and in accordance with the rock mass class. The rock mass classes in the study area of STA 8+869, 8+872 and 8+879 based on the RMR classification are included in rock class III, namely rocks that have moderate conditions (fair quality rock mass) and rock class IV, namely rocks that have poor conditions (poor quality rock mass). Meanwhile, the rock mass class based on the Q-System includes very poor rock mass.The recommendation for supporting the tunnel face according to RMR in the study area is to use a combination of rockbolt, shotcrete and wiremesh supports, while the support recommendation according to the Q-System uses a combination of rockbolt and shotcrete supports.

A Review on Machine Learning approaches for Himalayan Rock Mass Classification

Among all of the subsystems and components of hydropower, the tunnel is one of the crucial structures that face most of the pre-operation, real-time and post-operation challenges. Tunnels used as a water conveyance system in the majority of the projects lead to significant importance in the hydropower sector. The engineering rock mass classification is a crucial step in the accomplishment of underground constructions, particularly tunnels and caverns within the rock mass. The quality of rock mass parameters must be assessed and predicted with greater accuracy. However, the difficulties lie in the proper assessment of these parameters to define their quality. Studies have shown that despite the use of numerical and empirical methods in construction practices, numerous problems are reported when it comes to dealing with such structures. Over the years, various approaches based on machine learning (ML) techniques have been used for the minimization of these difficulties. As in recent trends, the development of machine learning technologies is currently bringing new methodologies in assessing rock mass classification. Although, because a sufficiently large database is required for such initiatives, there are a number of difficulties in their practical execution. Thus, a review of studies on the use of machine learning techniques in rock mass classification is presented in this paper. To determine the future directions of ML in RMC, it presents a comprehensive assessment to examine how ML models are being used. This study offers recommendations for creating an industry-specific model focusing on the strengths and weaknesses of ML techniques

A Qualitative Slope Stability Assessment of Hill Slopes Using Multiple Integrated Approaches Along Bafliaz – Poshiana (Mughal) Road, Jammu and Kashmir

Abstract Transport, communication and socio-economic activities in the hilly areas are completely dependent on the road network. The road network connecting Bafliaz to Shopian town (known as Mughal Road) plays a vital role in the transportation and other activities of the region. It connects the Kashmir Valley with Jammu city and is strategically very important to the country. Almost the entire road length of Mughal Road is vulnerable to landslide incidences and the events of occurrence/recurrence of landslides indicate that the probability of landslide/slope failure is higher along the road cut slopes during the event of antecedent and heavy rainfall and snowfall events. In order to assess the stability of hill slopes, it becomes of utmost importance to understand the failure mechanism along these slopes. In this regard, the slope stability assessment has been carried out by using multiple integrated approaches like the Markland test of kinematic analysis, Geological Strength Index (GSI), Rock mass classification (RMR), and Slope Mass Rating (SMR) at different locations. The type of failure and direction of potential failures for individual slopes have been identified. The obtained results of RMR vary from 23 to 38 after adjustment rating (poor rock mass), SMR from 0 to 54 (very bad to fair rock mass), CSMR from 0 to 53 (very bad to fair rock mass) and the GSI values range from 35 to 45 (poor to fair rock mass). Markland’s test of Kinematic check reveals that planar and wedge failures are the most common phenomenon along the Mughal Road sector.

Seismic Assessment of Underground Structures in the Weak Himalayan Rock Mass for Hydropower Development

This study focuses on the seismic assessment of underground structures in the transition of Higher Himalayan and Lesser Himalayan region of Nepal. Due to fragile geology of the Himalaya, most of the underground structures are constructed in the highly jointed rock mass. Rock mass classification approaches are extensively used for the estimation of supports without seismic consideration. Design of the underground structures like tunnels and cavern seismic effect is rarely considered in the Nepal Himalaya. For the sustainable development of hydropower project tunnel and caverns plays vital role in terms of its stability. Presence of faults, and crushed zone, along the alignment of tunnel, the existing in-situ stress increases beyond its critical level and strength. The long-term seismic behaviour of an underground structure is very essential in weak rock mass. A detailed dynamic analysis of underground response is evaluated using 2D numerical analysis with the geological data, rock mass and fault encountered in hydropower tunnel, through case study. A comparison is then made between the analysis result of the tunnel response with and without seismic consideration.

Geotechnical and Geophysical Investigations of Estimating Rock Mass Properties

Accurate assessments of the deformation and strength properties of rock masses are essential for analysing the foundations, subsurface excavation, and construction of slopes, however, identifying geotechnical properties, particularly weak rock masses, is challenging for geoscientists, geologists, and geotechnical investigators. To determine the rock mass properties, geophysical techniques, and rock mass classification methods are widely used. Although rock classification methodologies exist, they are rarely employed in foundation construction analysis due to being predominantly established for tunnelling purposes. This study aims to employ classification methods, and geophysical and geotechnical investigation approaches to provide realistic rock mass properties for a building foundation, compare results, and assess techniques' effectiveness; it also reviews current state-of-the-art methods for classification techniques and investigations. Results show that compressional and shear wave velocities increase with depth indicating a change from poor to satisfactory mechanical behaviour, and empirical equations provide highly valuable and satisfactory deformation modulus values when compared to field seismic values. Almost all classification methods provided reasonable results compared with the seismic results, however highly overestimated and underestimated outcomes were obtained from some empirical equations applying the Rock Quality Designation index and Rock Mass Rating system. Empirical equations that gave the practical deformation modulus outcomes are the Rock Mass Rating based equations and Q-based equations and Geological Strength Index-based equations. Overall, the Rock Quality Designation index underestimated the seismic result, whereas the Rock Mass Rating, Q, and Geological Strength Index systems produced good agreement and realistic results. To conclude, classification methods were effectively applied to a building foundation. Since the Rock Quality Designation is a key value and feature for other systems including Q, and Rock Mass Rating systems, it is an essential factor. Although the Q systems provided practical consequences, this system mainly relied on the Rock Quality Designation value not the compressive strength of the intact rock. The Rock Mass Rating and being the most feasible and essential systems due to detailed geotechnical observations as inputs more significantly the uniaxial compressive strength of the intact rock, and various factors including deformation modulus, strength, Poisson’s ratio, equivalent cohesion, and friction angle of rock mass as outcomes.

Stability analysis and support requirements for haulage drift in the vicinity of mined stopes

In this study, combined empirical, numerical, and in-situ monitoring methods were combined to carry out stability analyses and support design for a haulage drift subjected to mining activity. The rock mass quality of the haulage drift was characterized by the RMR, Q, and GSI, and the rock mass properties were calculated. The support requirements for haulage drift during mining were determined by rock mass classification systems. RS2 was used to analyze the plastic zone and displacement of the haulage drift during mining. After the stope was mined, the surrounding rock exhibited a butterfly plastic zone with an asymmetric distribution, and the roof damage was most severe near the stope side. Overall, the haulage drift tended to move in the stope direction, which is consistent with engineering expectations. The support systems determined using the empirical method were analyzed using RS2 and UNWEDGE software. The maximum plastic zone depth of the roof decreased from 4.2 to 2.01 m, and the safety factor of the unstable wedge block increased from 0 to 10.2 after support. In-situ drilling detection shows that the failure depth of the haulage drift roof is 2.37 m. Therefore, a combination of empirical, numerical, and in-situ monitoring methods can be effective for quantitative stability assessments and support design optimization of haulage drifts in the vicinity of mined stopes.

Estimation of the Poisson's Ratio of the Rock Mass

The value of Poisson's ratio is a crucial parameter in rock mechanics and engineering for both intact rock and rock mass. Poisson's ratio has not gotten the attention it merits compared to other essential mechanical characteristics of intact rock and rock mass. Limited relationships exist between rock mass classification systems (such as RMR, RMQR, Q, and GSI) and Poisson's ratio. This paper provides a comprehensive review of models proposed by various researchers for estimating Poisson's ratio for rock mass. The different methods are compared, and new general equations are derived. The results indicate that the Poisson's ratio value of rock mass is inversely proportional to its quality and strength and depends on the Poisson's ratio value of the intact rock. Specifically, a linear equation is obtained using the RMR or GSI system, showing that the Poisson's ratio increases as the quality and strength of the rock mass decrease. The Q system has a logarithmic link between the rock mass quality and Poisson's ratio. It should be noted that the derived equations are applicable only under the assumption of a homogeneous isotropic rock mass.

Developing rock mass classification method using precise description of joints

Developing rock mass classification method using precise description of joints

Prediction of uniaxial compressive strength of rock based on lithology using stacking models

Uniaxial compressive strength (UCS) of rock is an essential parameter in geotechnical engineering. Point load strength (PLS), P-wave velocity, and Schmidt hammer rebound number (SH) are more easily obtained than UCS and are extensively applied for the indirect estimation of UCS. This study collected 1080 datasets consisting of SH, P-wave velocity, PLS, and UCS. All datasets were integrated into three categories (sedimentary, igneous, and metamorphic rocks) according to lithology. Stacking models combined with tree-based models and linear regression were developed based on the datasets of three rock types. Model evaluation showed that the stacking model combined with random forest and linear regression was the optimal model for three rock types. UCS of metamorphic rocks was less predictable than that of sedimentary and igneous rocks. Nonetheless, the proposed stacking models can improve the predictive performance for UCS of metamorphic rocks. The developed predictive models can be applied to quickly predict UCS at engineering sites, which benefits the rapid and intelligent classification of rock masses. Moreover, the importance of SH, P-wave velocity, and PLS were analyzed for the estimation of UCS. SH was a reliable indicator for UCS evaluation across various rock types. P-wave velocity was a valid parameter for evaluating the UCS of igneous rocks, but it was not reliable for assessing the UCS of metamorphic rocks.

A quantitative assessment method for the correlation between rock mass basic quality and Protodyakonov coefficient

With the implementation of the “Belt and Road” initiative, tunnel projects were designed in accordance with Russian design specifications and constructed by Chinese companies in countries along the route. Design companies and construction companies use different rock classification methods, which will increase the safety risk and cost of the tunnels. Therefore, it is necessary to study the correlation between Chinese engineering rock mass classification and Russian rock mass classification. The goal was to establish the correlation between rock mass basic quality (BQ) and Protodyakonov coefficient (f) for rock mass classification. Firstly, based on the equivalence principle of uniaxial compressive strength (UCS), the relationship between BQ and f containing the velocity index of rock mass (Kv) was established by comparing the relationship between f and BQ in the empirical formula of UCS. Secondly, through the equivalent principle of volumetric joint count of rock mass (Jv), we compared the empirical formula between the intactness index of Kv and rock quality designation (RQD) and conducted linear regression analysis on a large amount of data. The relationship between Kv and RQD was established. Finally, the relationship between BQ and f is quantitatively expressed and a classification table corresponding to the Russian rock classification and the Chinese engineering rock classification is obtained. The method is applied to Kyrgyzstan mountain tunnels.

Tunnel Stability Analysis Under Seismic Load Using Finite Element Method: A Case Study of Spillway Tunnel, Sidan Dam, Bali, Indonesia

The location of the Sidan Spillway Tunnel, as shown on the Earthquake Hazard Zone Map of Bali Island, is in a moderate hazard zone. The support system of the spillway tunnel of the Sidan Dam, Indonesia, was empirically designed based on rock mass classification systems. The objective of this research was to verify the performance of the proposed support system by a numerical method considering the uncertainty in using rock mass classification systems for designing a tunnel support system. In addition, being located in an active seismic region, the seismic load was not considered in the empirical tunnel support design. The proposed tunnel support design was numerically modeled in two dimensions using a finite element method and subjected to a seismic load obtained from probabilistic seismic hazard analysis. A numerical analysis without seismic load was also conducted for comparison. Phase2 software was used for the tunnel stability analysis. The input parameters in the numerical analysis were derived from field investigations (surface engineering geological mapping and evaluation of drilling cores) and laboratory tests. The spillway tunnel area consists of pyroclastic rocks, and the Generalized Hoek-Brown Failure Criterion was selected for the strength parameter of rocks. The suggested support designs are systematic rock bolts (20 mm diameter fully grouted), wired mesh, shotcrete, and steel ribs at specific locations. The numerical models determined that applying the proposed tunnel support system recommended based on the empirical method resulted in a roof strength factor of more than 1.5 under static load & 1.1 under seismic load and roof displacements lower than a 10 cm maximum displacement at every stage of construction. The results verify that the tunnel construction will be stable and fulfill the required safety standard, both under and without seismic loads.